{
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"
    }
   },
   "cell_type": "markdown",
   "id": "06f2cb2a-6b9a-45d8-8569-4c0312b1ac63",
   "metadata": {},
   "source": [
    "## 消费者的基本概念与使用\n",
    "\n",
    "与生产者对应的是消费者，应用程序可以通过 KafkaConsumer 订阅主题，并从订阅的主题中拉取消息\n",
    "\n",
    "### 消费者与消费组\n",
    "与其他一些消息中间件不同的是：在 Kafka 的消费者理念上还有一层消费组（Consumer Group）的概念，每个消费者都有一个对应的消费组，每个消费组之间互不影响，独立消费主题中的信息\n",
    "\n",
    "![image.png](attachment:01b6a629-a13c-4d99-9919-f2318c4bc803.png)\n",
    "\n",
    "如上图所示，某个主题中共有4个分区（Partition）：P0、P1、P2、P3。有两个消费组A和B都订阅了这个主题，消费组A中有4个消费者（C0、C1、C2和C3），消费组B中有2个消费者（C4和C5）。按照 Kafka 默认的规则，最后的分配结果是消费组A中的每一个消费者分配到1个分区，消费组B中的每一个消费者分配到2个分区，两个消费组之间互不影响。每个消费者消费所分配到的分区中的消息。换言之，**每一个分区只能被一个消费组中的一个消费者所消费**\n",
    "\n",
    "一般有两种消息投递模式：点对点（P2P，Point-to-Point）模式和发布/订阅（Pub/Sub）模式。\n",
    "\n",
    "* 点对点模式是基于队列的，消息生产者发送消息到队列，消息消费者从队列中接收消息\n",
    "* 发布订阅模式定义了如何向一个内容节点发布和订阅消息，这个内容节点称为主题（Topic），主题可以认为是消息传递的中介，消息发布者将消息发布到某个主题，而消息订阅者从主题中订阅消息。\n",
    "\n",
    "kafka 借助消费组的概念可以同时支持两种模式的消息订阅模式：\n",
    "* 如果所有的消费者都隶属于同一个消费组，那么所有的消息都会被均衡地投递给每一个消费者，即每条消息只会被一个消费者处理，这就相当于点对点模式的应用。\n",
    "* \r\n",
    "如果所有的消费者都隶属于不同的消费组，那么所有的消息都会被广播给所有的消费者，即每条消息会被所有的消费者处理，这就相当于发布/订阅模式的应用。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ca070d3-35e1-400c-918f-0066e00abc0d",
   "metadata": {},
   "source": [
    "### 消费者客户端的使用\n",
    "\n",
    "一个正常的消费逻辑需要具备以下几个步骤：\n",
    "\n",
    "1. 配置消费者客户端参数及创建相应的消费者实例，并订阅主题\n",
    "3. 拉取消息并消费\n",
    "4. 提交消费位移\n",
    "5. 关闭消费者实例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9950bd7b-30e7-4d4f-b2be-29a52f069adc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m \u001b[0mKafkaConsumer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mtopics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mconfigs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "Consume records from a Kafka cluster.\n",
       "\n",
       "The consumer will transparently handle the failure of servers in the Kafka\n",
       "cluster, and adapt as topic-partitions are created or migrate between\n",
       "brokers. It also interacts with the assigned kafka Group Coordinator node\n",
       "to allow multiple consumers to load balance consumption of topics (requires\n",
       "kafka >= 0.9.0.0).\n",
       "\n",
       "The consumer is not thread safe and should not be shared across threads.\n",
       "\n",
       "Arguments:\n",
       "    *topics (str): optional list of topics to subscribe to. If not set,\n",
       "        call :meth:`~kafka.KafkaConsumer.subscribe` or\n",
       "        :meth:`~kafka.KafkaConsumer.assign` before consuming records.\n",
       "\n",
       "Keyword Arguments:\n",
       "    bootstrap_servers: 'host[:port]' string (or list of 'host[:port]'\n",
       "        strings) that the consumer should contact to bootstrap initial\n",
       "        cluster metadata. This does not have to be the full node list.\n",
       "        It just needs to have at least one broker that will respond to a\n",
       "        Metadata API Request. Default port is 9092. If no servers are\n",
       "        specified, will default to localhost:9092.\n",
       "    client_id (str): A name for this client. This string is passed in\n",
       "        each request to servers and can be used to identify specific\n",
       "        server-side log entries that correspond to this client. Also\n",
       "        submitted to GroupCoordinator for logging with respect to\n",
       "        consumer group administration. Default: 'kafka-python-{version}'\n",
       "    group_id (str or None): The name of the consumer group to join for dynamic\n",
       "        partition assignment (if enabled), and to use for fetching and\n",
       "        committing offsets. If None, auto-partition assignment (via\n",
       "        group coordinator) and offset commits are disabled.\n",
       "        Default: None\n",
       "    key_deserializer (callable): Any callable that takes a\n",
       "        raw message key and returns a deserialized key.\n",
       "    value_deserializer (callable): Any callable that takes a\n",
       "        raw message value and returns a deserialized value.\n",
       "    fetch_min_bytes (int): Minimum amount of data the server should\n",
       "        return for a fetch request, otherwise wait up to\n",
       "        fetch_max_wait_ms for more data to accumulate. Default: 1.\n",
       "    fetch_max_wait_ms (int): The maximum amount of time in milliseconds\n",
       "        the server will block before answering the fetch request if\n",
       "        there isn't sufficient data to immediately satisfy the\n",
       "        requirement given by fetch_min_bytes. Default: 500.\n",
       "    fetch_max_bytes (int): The maximum amount of data the server should\n",
       "        return for a fetch request. This is not an absolute maximum, if the\n",
       "        first message in the first non-empty partition of the fetch is\n",
       "        larger than this value, the message will still be returned to\n",
       "        ensure that the consumer can make progress. NOTE: consumer performs\n",
       "        fetches to multiple brokers in parallel so memory usage will depend\n",
       "        on the number of brokers containing partitions for the topic.\n",
       "        Supported Kafka version >= 0.10.1.0. Default: 52428800 (50 MB).\n",
       "    max_partition_fetch_bytes (int): The maximum amount of data\n",
       "        per-partition the server will return. The maximum total memory\n",
       "        used for a request = #partitions * max_partition_fetch_bytes.\n",
       "        This size must be at least as large as the maximum message size\n",
       "        the server allows or else it is possible for the producer to\n",
       "        send messages larger than the consumer can fetch. If that\n",
       "        happens, the consumer can get stuck trying to fetch a large\n",
       "        message on a certain partition. Default: 1048576.\n",
       "    request_timeout_ms (int): Client request timeout in milliseconds.\n",
       "        Default: 305000.\n",
       "    retry_backoff_ms (int): Milliseconds to backoff when retrying on\n",
       "        errors. Default: 100.\n",
       "    reconnect_backoff_ms (int): The amount of time in milliseconds to\n",
       "        wait before attempting to reconnect to a given host.\n",
       "        Default: 50.\n",
       "    reconnect_backoff_max_ms (int): The maximum amount of time in\n",
       "        milliseconds to backoff/wait when reconnecting to a broker that has\n",
       "        repeatedly failed to connect. If provided, the backoff per host\n",
       "        will increase exponentially for each consecutive connection\n",
       "        failure, up to this maximum. Once the maximum is reached,\n",
       "        reconnection attempts will continue periodically with this fixed\n",
       "        rate. To avoid connection storms, a randomization factor of 0.2\n",
       "        will be applied to the backoff resulting in a random range between\n",
       "        20% below and 20% above the computed value. Default: 1000.\n",
       "    max_in_flight_requests_per_connection (int): Requests are pipelined\n",
       "        to kafka brokers up to this number of maximum requests per\n",
       "        broker connection. Default: 5.\n",
       "    auto_offset_reset (str): A policy for resetting offsets on\n",
       "        OffsetOutOfRange errors: 'earliest' will move to the oldest\n",
       "        available message, 'latest' will move to the most recent. Any\n",
       "        other value will raise the exception. Default: 'latest'.\n",
       "    enable_auto_commit (bool): If True , the consumer's offset will be\n",
       "        periodically committed in the background. Default: True.\n",
       "    auto_commit_interval_ms (int): Number of milliseconds between automatic\n",
       "        offset commits, if enable_auto_commit is True. Default: 5000.\n",
       "    default_offset_commit_callback (callable): Called as\n",
       "        callback(offsets, response) response will be either an Exception\n",
       "        or an OffsetCommitResponse struct. This callback can be used to\n",
       "        trigger custom actions when a commit request completes.\n",
       "    check_crcs (bool): Automatically check the CRC32 of the records\n",
       "        consumed. This ensures no on-the-wire or on-disk corruption to\n",
       "        the messages occurred. This check adds some overhead, so it may\n",
       "        be disabled in cases seeking extreme performance. Default: True\n",
       "    metadata_max_age_ms (int): The period of time in milliseconds after\n",
       "        which we force a refresh of metadata, even if we haven't seen any\n",
       "        partition leadership changes to proactively discover any new\n",
       "        brokers or partitions. Default: 300000\n",
       "    partition_assignment_strategy (list): List of objects to use to\n",
       "        distribute partition ownership amongst consumer instances when\n",
       "        group management is used.\n",
       "        Default: [RangePartitionAssignor, RoundRobinPartitionAssignor]\n",
       "    max_poll_records (int): The maximum number of records returned in a\n",
       "        single call to :meth:`~kafka.KafkaConsumer.poll`. Default: 500\n",
       "    max_poll_interval_ms (int): The maximum delay between invocations of\n",
       "        :meth:`~kafka.KafkaConsumer.poll` when using consumer group\n",
       "        management. This places an upper bound on the amount of time that\n",
       "        the consumer can be idle before fetching more records. If\n",
       "        :meth:`~kafka.KafkaConsumer.poll` is not called before expiration\n",
       "        of this timeout, then the consumer is considered failed and the\n",
       "        group will rebalance in order to reassign the partitions to another\n",
       "        member. Default 300000\n",
       "    session_timeout_ms (int): The timeout used to detect failures when\n",
       "        using Kafka's group management facilities. The consumer sends\n",
       "        periodic heartbeats to indicate its liveness to the broker. If\n",
       "        no heartbeats are received by the broker before the expiration of\n",
       "        this session timeout, then the broker will remove this consumer\n",
       "        from the group and initiate a rebalance. Note that the value must\n",
       "        be in the allowable range as configured in the broker configuration\n",
       "        by group.min.session.timeout.ms and group.max.session.timeout.ms.\n",
       "        Default: 10000\n",
       "    heartbeat_interval_ms (int): The expected time in milliseconds\n",
       "        between heartbeats to the consumer coordinator when using\n",
       "        Kafka's group management facilities. Heartbeats are used to ensure\n",
       "        that the consumer's session stays active and to facilitate\n",
       "        rebalancing when new consumers join or leave the group. The\n",
       "        value must be set lower than session_timeout_ms, but typically\n",
       "        should be set no higher than 1/3 of that value. It can be\n",
       "        adjusted even lower to control the expected time for normal\n",
       "        rebalances. Default: 3000\n",
       "    receive_buffer_bytes (int): The size of the TCP receive buffer\n",
       "        (SO_RCVBUF) to use when reading data. Default: None (relies on\n",
       "        system defaults). The java client defaults to 32768.\n",
       "    send_buffer_bytes (int): The size of the TCP send buffer\n",
       "        (SO_SNDBUF) to use when sending data. Default: None (relies on\n",
       "        system defaults). The java client defaults to 131072.\n",
       "    socket_options (list): List of tuple-arguments to socket.setsockopt\n",
       "        to apply to broker connection sockets. Default:\n",
       "        [(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]\n",
       "    consumer_timeout_ms (int): number of milliseconds to block during\n",
       "        message iteration before raising StopIteration (i.e., ending the\n",
       "        iterator). Default block forever [float('inf')].\n",
       "    security_protocol (str): Protocol used to communicate with brokers.\n",
       "        Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.\n",
       "        Default: PLAINTEXT.\n",
       "    ssl_context (ssl.SSLContext): Pre-configured SSLContext for wrapping\n",
       "        socket connections. If provided, all other ssl_* configurations\n",
       "        will be ignored. Default: None.\n",
       "    ssl_check_hostname (bool): Flag to configure whether ssl handshake\n",
       "        should verify that the certificate matches the brokers hostname.\n",
       "        Default: True.\n",
       "    ssl_cafile (str): Optional filename of ca file to use in certificate\n",
       "        verification. Default: None.\n",
       "    ssl_certfile (str): Optional filename of file in pem format containing\n",
       "        the client certificate, as well as any ca certificates needed to\n",
       "        establish the certificate's authenticity. Default: None.\n",
       "    ssl_keyfile (str): Optional filename containing the client private key.\n",
       "        Default: None.\n",
       "    ssl_password (str): Optional password to be used when loading the\n",
       "        certificate chain. Default: None.\n",
       "    ssl_crlfile (str): Optional filename containing the CRL to check for\n",
       "        certificate expiration. By default, no CRL check is done. When\n",
       "        providing a file, only the leaf certificate will be checked against\n",
       "        this CRL. The CRL can only be checked with Python 3.4+ or 2.7.9+.\n",
       "        Default: None.\n",
       "    ssl_ciphers (str): optionally set the available ciphers for ssl\n",
       "        connections. It should be a string in the OpenSSL cipher list\n",
       "        format. If no cipher can be selected (because compile-time options\n",
       "        or other configuration forbids use of all the specified ciphers),\n",
       "        an ssl.SSLError will be raised. See ssl.SSLContext.set_ciphers\n",
       "    api_version (tuple): Specify which Kafka API version to use. If set to\n",
       "        None, the client will attempt to infer the broker version by probing\n",
       "        various APIs. Different versions enable different functionality.\n",
       "\n",
       "        Examples:\n",
       "            (0, 9) enables full group coordination features with automatic\n",
       "                partition assignment and rebalancing,\n",
       "            (0, 8, 2) enables kafka-storage offset commits with manual\n",
       "                partition assignment only,\n",
       "            (0, 8, 1) enables zookeeper-storage offset commits with manual\n",
       "                partition assignment only,\n",
       "            (0, 8, 0) enables basic functionality but requires manual\n",
       "                partition assignment and offset management.\n",
       "\n",
       "        Default: None\n",
       "    api_version_auto_timeout_ms (int): number of milliseconds to throw a\n",
       "        timeout exception from the constructor when checking the broker\n",
       "        api version. Only applies if api_version set to None.\n",
       "    connections_max_idle_ms: Close idle connections after the number of\n",
       "        milliseconds specified by this config. The broker closes idle\n",
       "        connections after connections.max.idle.ms, so this avoids hitting\n",
       "        unexpected socket disconnected errors on the client.\n",
       "        Default: 540000\n",
       "    metric_reporters (list): A list of classes to use as metrics reporters.\n",
       "        Implementing the AbstractMetricsReporter interface allows plugging\n",
       "        in classes that will be notified of new metric creation. Default: []\n",
       "    metrics_num_samples (int): The number of samples maintained to compute\n",
       "        metrics. Default: 2\n",
       "    metrics_sample_window_ms (int): The maximum age in milliseconds of\n",
       "        samples used to compute metrics. Default: 30000\n",
       "    selector (selectors.BaseSelector): Provide a specific selector\n",
       "        implementation to use for I/O multiplexing.\n",
       "        Default: selectors.DefaultSelector\n",
       "    exclude_internal_topics (bool): Whether records from internal topics\n",
       "        (such as offsets) should be exposed to the consumer. If set to True\n",
       "        the only way to receive records from an internal topic is\n",
       "        subscribing to it. Requires 0.10+ Default: True\n",
       "    sasl_mechanism (str): Authentication mechanism when security_protocol\n",
       "        is configured for SASL_PLAINTEXT or SASL_SSL. Valid values are:\n",
       "        PLAIN, GSSAPI, OAUTHBEARER, SCRAM-SHA-256, SCRAM-SHA-512.\n",
       "    sasl_plain_username (str): username for sasl PLAIN and SCRAM authentication.\n",
       "        Required if sasl_mechanism is PLAIN or one of the SCRAM mechanisms.\n",
       "    sasl_plain_password (str): password for sasl PLAIN and SCRAM authentication.\n",
       "        Required if sasl_mechanism is PLAIN or one of the SCRAM mechanisms.\n",
       "    sasl_kerberos_service_name (str): Service name to include in GSSAPI\n",
       "        sasl mechanism handshake. Default: 'kafka'\n",
       "    sasl_kerberos_domain_name (str): kerberos domain name to use in GSSAPI\n",
       "        sasl mechanism handshake. Default: one of bootstrap servers\n",
       "    sasl_oauth_token_provider (AbstractTokenProvider): OAuthBearer token provider\n",
       "        instance. (See kafka.oauth.abstract). Default: None\n",
       "\n",
       "Note:\n",
       "    Configuration parameters are described in more detail at\n",
       "    https://kafka.apache.org/documentation/#consumerconfigs\n",
       "\u001b[0;31mFile:\u001b[0m           /opt/conda/lib/python3.11/site-packages/kafka/consumer/group.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topic = \"test_topic\"\n",
    "\n",
    "from kafka import KafkaConsumer\n",
    "\n",
    "KafkaConsumer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "70fa0d52-4ca0-4dd4-93b6-db609bdaad92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建消费者（具体参数细则请查看文档）\n",
    "import json \n",
    "consumer = KafkaConsumer(topic, \n",
    "                         bootstrap_servers=[\"kafka1:9092\"], \n",
    "                         group_id='my-group',\n",
    "                         value_deserializer=lambda v: json.loads(v.decode()),\n",
    "                         auto_offset_reset='earliest',\n",
    "                         enable_auto_commit=True,\n",
    "                         auto_commit_interval_ms=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45590829-a4ca-4251-95f5-46aad75bbda5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 拉取消息并消费\n",
    "for message in consumer:\n",
    "    value = message.value\n",
    "    headers = message.headers\n",
    "    # 处理消息\n",
    "    print(value, message.)\n",
    "    for header in headers:\n",
    "        print(header[0], header[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55bdb04e-9240-4abf-be93-471f49d69940",
   "metadata": {},
   "source": [
    "#### 手动提交位移 \n",
    "```python\n",
    "#提交当前消费者组中所有分区的位移\n",
    "consumer.commit()\n",
    "# 支持提交特定分区的位移，可以将分区信息作为参数传递给commit()方法\n",
    "consumer.commit({TopicPartition('my_topic', 0): OffsetAndMetadata(1234, None)})\n",
    "# 提交my_topic主题下第0个分区的位移为1234的消息的位移信息\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ad8f34e-5685-4694-a620-459c4d10eea6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mconsumer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mautocommit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Close the consumer, waiting indefinitely for any needed cleanup.\n",
       "\n",
       "Keyword Arguments:\n",
       "    autocommit (bool): If auto-commit is configured for this consumer,\n",
       "        this optional flag causes the consumer to attempt to commit any\n",
       "        pending consumed offsets prior to close. Default: True\n",
       "\u001b[0;31mFile:\u001b[0m      /opt/conda/lib/python3.11/site-packages/kafka/consumer/group.py\n",
       "\u001b[0;31mType:\u001b[0m      method"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#关闭消费者\n",
    "consumer.close?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58ffffcc-002c-4d66-b469-fab85ee34731",
   "metadata": {},
   "source": [
    "#### 注意！！！\n",
    "\n",
    "消费者会有重复消费和消息丢失的风险，给出工程界的解决方案 [如何保证kafka无消息丢失](https://mp.weixin.qq.com/s/MzGnUHRDWihYFT4epAXOOw)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "949af917-c235-4e06-aaf1-9cdf0a16e87f",
   "metadata": {},
   "source": [
    "## 再均衡器与拦截器\n",
    "\n",
    "### 再均衡器\n",
    "再均衡是指分区的所属权从一个消费者转移到另一消费者的行为，它为消费组具备高可用性和伸缩性提供保障，使我们可以既方便又安全地删除消费组内的消费者或往消费组内添加消费者\n",
    "\n",
    "当一个分区被重新分配给另一个消费者时，消费者当前的状态也会丢失。比如消费者消费完某个分区中的一部分消息时还没有来得及提交消费位移就发生了再均衡操作，之后这个分区又被分配给了消费组内的另一个消费者，原来被消费完的那部分消息又被重新消费一遍，可能会导致重复消费\n",
    "\n",
    "### 拦截器\n",
    "费者拦截器主要在消费到消息或在提交消费位移时进行一些定制化的操作，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "213d3eb2-47f8-46b5-8cb7-22a0427f5fb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kafka import KafkaConsumer, ConsumerRebalanceListener\n",
    "\n",
    "class MyConsumerInterceptor(object):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def on_consume(self, message):\n",
    "        # 在消息被消费者消费之前，对消息进行定制化操作\n",
    "        return message\n",
    "    \n",
    "    def on_commit(self, message):\n",
    "        # 会在提交完消费位移之后调用拦截器的 onCommit() 方法\n",
    "        return message\n",
    "\n",
    "class MyConsumerRebalanceListener(ConsumerRebalanceListener):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def on_partitions_revoked(partitions):\n",
    "        # 在分区再平衡之前，将分区的偏移量提交到Kafka集群\n",
    "        for partition in partitions:\n",
    "            consumer.commit(offsets={TopicPartition(partition.topic, partition.partition): partition.offset})\n",
    "\n",
    "    def on_partitions_assigned(partitions):\n",
    "        # 在分区重新分配之后，输出分配给当前消费者的分区信息\n",
    "        print('Partitions assigned:', partitions)\n",
    "\n",
    "consumer = KafkaConsumer(\n",
    "    'my_topic',\n",
    "    bootstrap_servers=['localhost:9092'],\n",
    "    auto_offset_reset='earliest',\n",
    "    enable_auto_commit=True,\n",
    "    group_id='my-group',\n",
    "    interceptor_classes=[MyConsumerInterceptor],\n",
    "    listener=MyConsumerRebalanceListener()\n",
    ")\n",
    "\n",
    "for message in consumer:\n",
    "    print(message)\n"
   ]
  }
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